Competency Framework Development for Genomics Nurse Educators
Bibliographic record
Abstract
BACKGROUND: As genomics becomes increasingly integral to health care, enhancing nurse educators' competence in teaching genomics is vital for sustaining nursing's role in precision health. PROBLEM: Many nurses lack confidence in applying genomics in practice, highlighting the need for improved genomics nursing education. APPROACH: The International Society for Nurses in Genetics convened a steering committee to develop a competency framework defining the role of Genomics Nurse Educators. We applied the Six-Step Model for Competency Framework Development in Healthcare Professions, drawing on targeted literature review and international stakeholder input to draft the framework. OUTCOMES: The resulting framework includes 3 domains and 7 competency areas defining the knowledge, expertise, and leadership required for Genomics Nurse Educators. CONCLUSIONS: The framework advances genomic nursing education globally, transitioning it from an emerging to an evolving specialty; provides a structured pathway for faculty development, supports integration of genomics into curricula, and promotes education of genomics-informed nurses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".